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class AliKMeansClustering: public TObject

Function Members (Methods)

public:
AliKMeansClustering()
AliKMeansClustering(const AliKMeansClustering&)
virtual~AliKMeansClustering()
voidTObject::AbstractMethod(const char* method) const
virtual voidTObject::AppendPad(Option_t* option = "")
virtual voidTObject::Browse(TBrowser* b)
static TClass*Class()
virtual const char*TObject::ClassName() const
virtual voidTObject::Clear(Option_t* = "")
virtual TObject*TObject::Clone(const char* newname = "") const
virtual Int_tTObject::Compare(const TObject* obj) const
virtual voidTObject::Copy(TObject& object) const
static Double_td(Double_t mx, Double_t my, Double_t x, Double_t y)
virtual voidTObject::Delete(Option_t* option = "")MENU
virtual Int_tTObject::DistancetoPrimitive(Int_t px, Int_t py)
virtual voidTObject::Draw(Option_t* option = "")
virtual voidTObject::DrawClass() constMENU
virtual TObject*TObject::DrawClone(Option_t* option = "") constMENU
virtual voidTObject::Dump() constMENU
virtual voidTObject::Error(const char* method, const char* msgfmt) const
virtual voidTObject::Execute(const char* method, const char* params, Int_t* error = 0)
virtual voidTObject::Execute(TMethod* method, TObjArray* params, Int_t* error = 0)
virtual voidTObject::ExecuteEvent(Int_t event, Int_t px, Int_t py)
virtual voidTObject::Fatal(const char* method, const char* msgfmt) const
virtual TObject*TObject::FindObject(const char* name) const
virtual TObject*TObject::FindObject(const TObject* obj) const
virtual Option_t*TObject::GetDrawOption() const
static Long_tTObject::GetDtorOnly()
virtual const char*TObject::GetIconName() const
virtual const char*TObject::GetName() const
virtual char*TObject::GetObjectInfo(Int_t px, Int_t py) const
static Bool_tTObject::GetObjectStat()
virtual Option_t*TObject::GetOption() const
virtual const char*TObject::GetTitle() const
virtual UInt_tTObject::GetUniqueID() const
virtual Bool_tTObject::HandleTimer(TTimer* timer)
virtual ULong_tTObject::Hash() const
virtual voidTObject::Info(const char* method, const char* msgfmt) const
virtual Bool_tTObject::InheritsFrom(const char* classname) const
virtual Bool_tTObject::InheritsFrom(const TClass* cl) const
virtual voidTObject::Inspect() constMENU
voidTObject::InvertBit(UInt_t f)
virtual TClass*IsA() const
virtual Bool_tTObject::IsEqual(const TObject* obj) const
virtual Bool_tTObject::IsFolder() const
Bool_tTObject::IsOnHeap() const
virtual Bool_tTObject::IsSortable() const
Bool_tTObject::IsZombie() const
virtual voidTObject::ls(Option_t* option = "") const
voidTObject::MayNotUse(const char* method) const
virtual Bool_tTObject::Notify()
voidTObject::Obsolete(const char* method, const char* asOfVers, const char* removedFromVers) const
static voidTObject::operator delete(void* ptr)
static voidTObject::operator delete(void* ptr, void* vp)
static voidTObject::operator delete[](void* ptr)
static voidTObject::operator delete[](void* ptr, void* vp)
void*TObject::operator new(size_t sz)
void*TObject::operator new(size_t sz, void* vp)
void*TObject::operator new[](size_t sz)
void*TObject::operator new[](size_t sz, void* vp)
AliKMeansClustering&operator=(const AliKMeansClustering&)
static voidOptimalInit(Int_t k, Int_t n, const Double_t* x, const Double_t* y, Double_t* mx, Double_t* my)
virtual voidTObject::Paint(Option_t* option = "")
virtual voidTObject::Pop()
virtual voidTObject::Print(Option_t* option = "") const
virtual Int_tTObject::Read(const char* name)
virtual voidTObject::RecursiveRemove(TObject* obj)
voidTObject::ResetBit(UInt_t f)
virtual voidTObject::SaveAs(const char* filename = "", Option_t* option = "") constMENU
virtual voidTObject::SavePrimitive(basic_ostream<char,char_traits<char> >& out, Option_t* option = "")
static voidSetBeta(Double_t beta)
voidTObject::SetBit(UInt_t f)
voidTObject::SetBit(UInt_t f, Bool_t set)
virtual voidTObject::SetDrawOption(Option_t* option = "")MENU
static voidTObject::SetDtorOnly(void* obj)
static voidTObject::SetObjectStat(Bool_t stat)
virtual voidTObject::SetUniqueID(UInt_t uid)
virtual voidShowMembers(TMemberInspector&)
static Int_tSoftKMeans(Int_t k, Int_t n, const Double_t* x, const Double_t* y, Double_t* mx, Double_t* my, Double_t* rk)
static Int_tSoftKMeans2(Int_t k, Int_t n, Double_t* x, Double_t* y, Double_t* mx, Double_t* my, Double_t* sigma2, Double_t* rk)
static Int_tSoftKMeans3(Int_t k, Int_t n, Double_t* x, Double_t* y, Double_t* mx, Double_t* my, Double_t* sigmax2, Double_t* sigmay2, Double_t* rk)
virtual voidStreamer(TBuffer&)
voidStreamerNVirtual(TBuffer& ClassDef_StreamerNVirtual_b)
virtual voidTObject::SysError(const char* method, const char* msgfmt) const
Bool_tTObject::TestBit(UInt_t f) const
Int_tTObject::TestBits(UInt_t f) const
virtual voidTObject::UseCurrentStyle()
virtual voidTObject::Warning(const char* method, const char* msgfmt) const
virtual Int_tTObject::Write(const char* name = 0, Int_t option = 0, Int_t bufsize = 0)
virtual Int_tTObject::Write(const char* name = 0, Int_t option = 0, Int_t bufsize = 0) const
protected:
virtual voidTObject::DoError(int level, const char* location, const char* fmt, va_list va) const
voidTObject::MakeZombie()

Data Members

protected:
static Double_tfBetabeta parameter

Class Charts

Inheritance Chart:
TObject
AliKMeansClustering

Function documentation

Int_t SoftKMeans(Int_t k, Int_t n, const Double_t* x, const Double_t* y, Double_t* mx, Double_t* my, Double_t* rk)
 The soft K-means algorithm

Int_t SoftKMeans2(Int_t k, Int_t n, Double_t* x, Double_t* y, Double_t* mx, Double_t* my, Double_t* sigma2, Double_t* rk)
 The soft K-means algorithm

Int_t SoftKMeans3(Int_t k, Int_t n, Double_t* x, Double_t* y, Double_t* mx, Double_t* my, Double_t* sigmax2, Double_t* sigmay2, Double_t* rk)
 The soft K-means algorithm

Double_t d(Double_t mx, Double_t my, Double_t x, Double_t y)
 Distance definition
 Quasi - Euclidian on the eta-phi cylinder
void OptimalInit(Int_t k, Int_t n, const Double_t* x, const Double_t* y, Double_t* mx, Double_t* my)
 Optimal initialisation using the k-means++ algorithm
 http://en.wikipedia.org/wiki/K-means%2B%2B

 k-means++ is an algorithm for choosing the initial values for k-means clustering in statistics and machine learning.
 It was proposed in 2007 by David Arthur and Sergei Vassilvitskii as an approximation algorithm for the NP-hard k-means problem---
 a way of avoiding the sometimes poor clusterings found by the standard k-means algorithm.


AliKMeansClustering()
{}
virtual ~AliKMeansClustering()
{}
void SetBeta(Double_t beta)
{fBeta = beta;}
AliKMeansResult& operator=(const AliKMeansClustering& )